Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI [0.03%]
使用可解释的人工智能堆叠集成学习模型增强自闭症谱系障碍的检测
Tao Song,Usama Jabbar,Valentin Marian Antohi et al.
Tao Song et al.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is manifested by sensory abnormalities such as hypersensitivity to sound and touch. Autistic children often have problems with communication, social interaction, and behav...
TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories [0.03%]
具有不确定性量化的时间感知序列聚类框架用于电子健康记录轨迹(TASC)
April Yujie Yan,Thomas K M Cudjoe,Casey Overby Taylor
April Yujie Yan
Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discovery and uncertainty quantification challenging. We developed a Time-Aware Sequence Clusteri...
Discovery of postoperative clinical states linked to cardiac surgery-associated acute kidney injury [0.03%]
术后临床状态与心脏手术相关急性肾损伤的关系发现
Motahare Shabestari,Vinod Kumar Chauhan,Mohammadtaghi Sarebanhassanabadi et al.
Motahare Shabestari et al.
Background: Cardiac surgery-associated acute kidney injury (AKI) remains a common postoperative complication and is associated with adverse short- and long-term outcomes. Conventional risk scores may have limited performa...
Parallel AI-driven framework for post-quantum secure medical image communication using swin-transformer restoration [0.03%]
基于Swin-Transformer恢复的用于后量子安全医学图像通信的并行AI驱动框架
Emad Alsuwat
Emad Alsuwat
Reliable and secure transmission of medical images is essential for telemedicine, remote diagnosis, and distributed healthcare systems. However, medical image communication over heterogeneous networks often suffers from packet loss, channel...
Deep learning multi-omics integration identifies new molecular subtypes of lung cancer [0.03%]
深度学习多组学整合识别新的肺癌分子亚型
Bianca Gonda,Derek Wang,Sayed Mehedi Azim et al.
Bianca Gonda et al.
Cancer is a heterogeneous disease, with numerous subtypes differing in molecular profiles, risk factors, clinical outcomes, and tumor locations. Lung cancer, the third most diagnosed cancer in the United States, is driven by a complex combi...
A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification [0.03%]
一种新的向量符号体系的图编码及其在基于病毒泛基因组的物种分类中的应用
Fabio Cumbo,Kabir Dhillon,Jayadev Joshi et al.
Fabio Cumbo et al.
Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolvi...
Automated candidate confounder scoping for adjustment in clinical research: a retrieval-augmented generation approach [0.03%]
临床研究中调整潜在混杂因素的自动化范围界定：一种检索增强生成方法
Jingjing Li,Kesong Wu,Xiao Wang et al.
Jingjing Li et al.
Background: Identifying confounding variables is fundamental for robust observational studies, yet the traditional manual process is a time-consuming and subjective barrier for researchers. Recent advances in Retrieval-Au...
Coxmos: interpretable survival models for high-dimensional and multi-omic data [0.03%]
CoxMos：高维和多组学数据的解释性生存模型
Pedro Salguero,Anabel Buendía-Galera,Sonia Tarazona
Pedro Salguero
Background: Survival analysis in high-dimensional (HD) and multi-block (MB) settings, such as omic and multi-omic studies, poses major methodological challenges due to multicollinearity, low events-per-variable ratios, an...
Correction: CancerHubs data Explorer: a web application for investigating mutation-enriched protein interaction hubs in human cancers [0.03%]
Correction: 癌症中心网站应用：一个用于探究人类癌症中突变丰富的蛋白质交互中心的数据探测器
Ivan Ferrari,Elisa Arsuffi,Stefano Biffo et al.
Ivan Ferrari et al.
Published Erratum
BioData mining. 2026 May 11;19(1):36. DOI:10.1186/s13040-026-00557-x 2026
Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis [0.03%]
一种基于EEG的精神分裂症诊断的自适应多阶段深度学习框架
Gran Badshah,Anurag Sinha,Himanshu Bansal et al.
Gran Badshah et al.
This study introduces a novel adaptive deep learning framework for EEG-based schizophrenia diagnosis that addresses the limitations of existing static classification models. Traditional approaches often fail to maintain diagnostic reliabili...